Life tables are the most precise instruments available to demographers for assessing overall mortality levels and trends in a population. Unfortunately, their construction is data-demanding: for both genders, and for each age x (from 0 to over 100), death risks (or rates) must be calculated. This requires a numerator (deaths) and a denominator (population), and results in more than 200 cells. If the observed population is small, random effects can easily predominate in one or more of these cells, producing unpredictable (and unreliable) results – even setting aside omissions, age misclassifications, and other forms of error. To be sure, a variety of methods exist to limit these distortions, but even they have their limits and can be stretched only so far. Is any analysis possible beyond that point?

Data, method and purpose

This question arose in our case. Exploiting an existing Istat database at municipal level (with a reconstruction of the series ensuring constant boundaries from 2002 to 2018 – a rare case indeed), we aimed to study small-scale mortality over the period across Italy’s almost 8,000 local administrative units (LAUs), some of them very small (down to 30 residents in the worst case). For that purpose, we needed a reliable and comparable measure of mortality, but also one that was not too demanding in terms of data input.

We therefore opted for the standardised mortality ratio (SMR), which is simply the ratio between observed and expected deaths. The former were available, thanks to the aforementioned Istat database, while the latter were calculated by applying a standard (national) mortality schedule to the local populations, broken down by gender and age.

Our SMR passed several preliminary tests, performing more than satisfactorily at the national level (Figure 1), as well as at the regional and provincial levels (NUTS2 and NUTS3, respectively – not shown here). Encouraged by this, we decided to apply it to Italy’s municipalities, the smallest possible territorial level (De Santis et al. 2025).

Our main curiosities (… oops, sorry. We meant: our main research questions) were:

1) How large were survival inequalities at the municipal level in Italy between 2002 and 2018?

2) How did these inequalities evolve over time?

3) Was any significant breakpoint detectable during this period?

4) Which contextual factors were most strongly associated with poor survival outcomes at the municipal level?

Main results

Figure 2 allows us to address our first three questions. Small-scale mortality variability was low in Italy during the period under examination (2002–2018): the coefficient of variation (i.e. the ratio between the standard deviation and the mean) ranged between 12% and 15% (panel a). On the negative side, it showed a slight upward trend, indicating somewhat greater relative heterogeneity (i.e. inequality) towards the end of the period.

No major breakpoints emerged over the 17 years we examined. Panel b of Figure 2 indicates that, of the total variance in LAU mortality, around 80% was attributable to within-region differences, and only about 20% to between-region differences. In other words, the frequently voiced concerns about wider inter-regional disparities following the regionalisation of the Italian health system – initiated in 1992 – appear to have been overstated, at least in terms of mortality. (It should be noted, however, that mortality is not the only metric by which health system performance should be assessed, but in our case, no other measures were available.)

Here too, however, a note of caution is warranted: although modest (around 20%, as noted), the between-region component of variability showed an upward trend. In other words, regions were more diverse at the end of the period than they had been at the beginning. Unfortunately, with our data, it is not possible to determine how much of this trend is attributable to policy choices (i.e. regionalisation of the health system) and how much to other factors.

Maps and correlations

Figure 3 visualises the distribution of small-scale mortality, averaged over the period 2002–2018. As is often the case in such analyses, the researchers’ hope of discerning a clear pattern is somewhat frustrated – particularly by the fact that male and female mortality do not appear to follow the same geographical distribution.

Lower mortality, especially among females, is typically observed in eastern Italy (Trentino–Alto Adige, the southern part of Veneto, Emilia-Romagna, Marche), in the centre (Umbria and Tuscany), and in southern Sardinia. Conversely, poorer survival conditions are found in south-western Italy (particularly the area around Naples and in Sicily), in the municipalities surrounding the capital city Rome (though not in Rome itself), and – somewhat unexpectedly – in numerous municipalities in the “affluent” North-West (Liguria, Valle d’Aosta, Piedmont, and parts of Lombardy), as well as in several municipalities along the river Po (running eastwards from Turin to just below Venice). Another area of concern can be identified in the north-east, covering parts of Friuli–Venezia Giulia and the northern municipalities of Veneto.

Regression results clearly indicate that mortality during the period was higher in municipalities characterised by greater “fragility” (an ad hoc index developed by Istat) and lower affluence (Table 1). They also show clear evidence of spillover effects between adjacent municipalities: mortality is geographically correlated (as captured by SAR models – neighbouring municipalities tend to exhibit similar levels), and so are the residuals (SEM models), suggesting that unobserved factors (e.g. air pollution, local policies, etc.) influence areas that extend beyond individual municipal boundaries.

Other potentially relevant variables – among the relatively few available in our database – did not yield consistent results. For instance, we expected mortality to be higher in municipalities with smaller and older populations, or those located in mountainous areas. This was not the case, or at least not consistently so: results varied by gender and proved difficult to interpret, at least for us.

Funding

We gratefully acknowledge financial support from:

– PRIN 2022 research project “The pre-Covid-19 stall in life expectancy in Italy: looking for explanations”, (n°2022CENE9F, PRIN 2022 call DD n.104 of 02/02/2022, funded by the European Union – NextGenerationEU)

– PRIN2022-PNRR research project “Foreign population and territory: integration processes, demographic imbalances, challenges and opportunities for the social and economic sustainability of the different local contexts (For.Pop.Ter)” [P2022 WNLM7], funded by European Union – Next Generation EU, component M4C2, Investment 1.1. 

* The views and opinions expressed are only those of the authors and may not reflect those of the financing institutions.

References

De Santis G., Benassi F., Carboni G., Maltagliati M. (2025). Local mortality patterns in Italy at the beginning of the 21st century. Genus, https://doi.org/10.1186/s41118-025-00262-3

The post Small-scale mortality in Italy: 2002–2018* appeared first on N-IUSSP.

Cultural distances are frequently evoked, especially by researchers who need to justify what their models cannot explain, but they are very hard to define and measure. When it comes to groups (in our case national groups: Italians and foreigners of various nationalities living in Italy) the very notion of cultural distance may be based on the false assumption of internal homogeneity.

Not very clear, is it? OK, let us try with an example. Consider Figure 1 and assume that the five cultural clusters ‘make sense’, i.e. that they correctly identify five different cultural typologies. Note that we ignore the cultural characteristics of each of these typologies, and we do not need to know them, at least initially. On the y axis we read proportions: among Italians (from the South and from the two main Islands) about 30% of respondents are of type A, ~25% are of type B, … and ~17% are of type E. As the proportions are approximately the same in the two cases, we conclude that the two groups of Italians are very similar, although not internally homogeneous.

Conversely, the Chinese living in Italy have a markedly distinct distribution of individuals across typologies: only about 4% are of type A, ~28% of type B, … and as many as ~45% of type E. Muslims (a short term for immigrants ‒ of unknown religion ‒ from predominantly Muslim countries) appear to be somewhere in-between, although in their case the share of typologies B (~30%) and C (~31%) is larger than in other instances.

Two things emerge from this analysis.

1. ‘National culture’ is not a specific typology. Each national group has (at least) five such typologies (and probably more, but in our case there are statistical indications that stopping at five categories is enough). What changes is the proportion of the members of that group (nation or area of origin) who can be classified in each of the five typologies.
2. Based on these proportions (better: on their differences), an estimate of the relative distance between (national) groups may be attempted.

An application to Italy (2011-2013)

This is the approach that we followed in a recent paper, with data taken from two similar ISTAT surveys conducted in Italy between 2011 and 2013, the latter covering the general population, the former focused on foreigners (De Santis, Maltagliati and Petrucci, 2021).

The main results of our analysis are displayed in Figure 2, which uses proportions (those of Figure 1, but for all groups) to calculate (Euclidean) distances, and then (with little loss of information, i.e. little distortion) forces these distances to appear on a Cartesian plane.

Italians form a homogeneous group, relatively speaking. However, within Italy, a clear north-to-south gradient appears. Northern Italians are culturally closer to immigrants from neighbouring European countries (France, Germany, and North-Central Europe). Southern Italians, instead, appear closer to other nationalities, starting with those around the Mediterranean Basin.

Some countries from homogeneous regions are highlighted, such as those from Latin America, or from the Indian peninsula. The fact that they are close, in Figure 2, means that their members have a comparable distribution among the five cultural typologies mentioned at the beginning.

The Chinese living in Italy are the culturally farthest immigrant group, according to our estimates, an outcome that conforms to expectations and to other sources (e.g. ISTAT 2020).

Finally, people of foreign origin, but who later acquired the Italian nationality (labelled ‘IT2’), lie somewhere in-between: they are somewhat detached from Italians with Italian origin, but they are closer to them than any other foreign group living in Italy.

What lies behind (or better, inside) our clusters

The construction of our five clusters is based on the 11 empirical indicators listed in Figure 3: how often certain ‘objects’ are used (e.g. personal computers), or certain activities are carried out (reading books, attending concerts, etc.) or politics is discussed. Admittedly, it is not culture proper that is being evaluated here: rather, it is the use of (free) time, which is influenced by culture but also by several other factors that we cannot control for (e.g. personal resources). Our indicators are less than ideal, we admit, but this is as far as we could go if we wanted to merge the two ISTAT surveys that were needed to compare foreigners (ISTAT 2016a) with Italians (ISTAT 2016b).

Figure 3 gives an idea of the main characteristics of the members within each cluster. For example, the members of cluster C (Secluded from the social world, if we want to label them) do few or none of the listed activities, similar in this respect to the members of cluster B (Semi-secluded), who are just slightly more active when it comes to dancing, or reading newspapers and magazines. (This was the case of immigrants from predominantly Islamic countries, remember?)

In cluster E (Surfers) we find individuals who frequently use PCs and surf the internet, go to the cinema, or to concerts (all types of live music) and go dancing, but who never talk about politics (as is the case for the Chinese, in Figure 1). The members of cluster A (Active) are quite or very active in all the activities indicated, and they also talk a lot about politics: many Italians are like that (see again Figure 1). Finally, there are the members of group D, the Committed: they talk a lot about politics, keep themselves decently informed, and disdain certain facets of modernity, such as PC, internet and discos.

However, one point should be emphasized: in any national group, there are individuals of all clusters (that is, of all typologies, which seems inconsistent with such notions as ‘national homogeneity’, ‘typical traits’, and so on: what changes is (merely?) the relative frequency of the various typologies.

What do we learn from all this?

First, we must caution readers against hasty interpretation. We refer them to the original article for the limitations of our findings, in terms, for instance, of available empirical indicators to assess the ‘cultural orientation’ of our respondents, clustering criteria, and impossibility of controlling for certain covariates (age, sex, education, income, etc.; De Santis, Maltagliati and Petrucci, 2021).

Also, we do not have any empirical indication of how time and selection influence our results. For instance, let us consider people of foreign origin who are now Italians. Acquiring the Italian nationality is a long and painful process. Therefore the ‘IT2’ group has, on average, a longer period of permanent stay in the country than any other group. The fact that they are culturally closer to Italians than all others may be interpreted in two main ways:

• they were selected from the start, and it is precisely this affinity that made them stay in the country and decide eventually to become Italians,
• they gradually got used to the ‘Italian way of thinking’, and their proximity to Italians at the end of the process indicates that people can and do change: cultural convergence is possible and is taking place.

Either way, cultural distances do not seem to be insurmountable obstacles when it comes to integrating foreigners in a host country. And, in all cases, those who evoke them can now start to measure them (if empirical data permit, of course).

Acknowledgements

Financial support is gratefully acknowledged from two sources:

1) JPI MYBL / CREW Project (Joint Programme Initiative: More Years Better Life, 2016 Call. CREW: Care, retirement and wellbeing of older people across different welfare regimes. MIUR Decree: n. 3266/2018; Official Bulletin no. 32 7. Feb 2019), and

2) MIUR-PRIN 2017 Grant (Italian Ministry of University and Research, Prot. N. 2017W5B55Y).

References

De Santis G., Maltagliati M., Petrucci A. (2021) So Close, So Far. The Cultural Distance of Foreigners in Italy. Social indicators Research, online first

ISTAT (2016a) Condizione e integrazione sociale dei cittadini stranieri: file per la ricerca,

ISTAT (2016b) Aspetti della vita quotidiana: file per la ricerca,

ISTAT (2020) Identità e percorsi di integrazione delle seconde generazioni in Italia Cultural identity and integration of second generations in Italy, Rome.

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